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Should the Advanced Measurement Approach for Operational Risk be Discarded? Evidence from the Chinese Banking Industry

Published on Mar 1, 2019in Review of Pacific Basin Financial Markets and Policies
· DOI :10.1142/s0219091519500073
Xiaoqian Zhu7
Estimated H-index: 7
(CAS: Chinese Academy of Sciences),
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences),
Dengsheng Wu10
Estimated H-index: 10
(CAS: Chinese Academy of Sciences)
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  • References (19)
  • Citations (0)
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References19
Newest
Yi-Kai Chen2
Estimated H-index: 2
(NUK: National University of Kaohsiung),
Chung-Hua Shen20
Estimated H-index: 20
(Shih Chien University)
+ 1 AuthorsChuan-Yi Yeh1
Estimated H-index: 1
This study employs an alternative measure of liquidity risk to investigate its determinants by using an unbalanced panel dataset of commercial banks in 12 advanced economies over the period 1994–2006. Dependence on liquid assets for external funding, supervisory and regulatory factors, and macroeconomic factors are all determinants of liquidity risk. Because of higher funding costs for obtaining liquidity, liquidity risk is regarded as a discount for bank profitability, yet liquidity risk shows ...
13 Citations Source Cite
Published on Jan 1, 2017in Journal of Operational Risk 0.73
Patrick J. McConnell5
Estimated H-index: 5
Recently, the Basel Committee on Banking Supervision (BCBS) published a consultation paper that proposed a radical change to the methodologies permitted for estimating operational risk regulatory capital (ORRC). The Committee proposed replacing all existing approaches with a single new formula, the standardized measurement approach (SMA), which, it is claimed, will be simpler and promote comparability of estimated ORRC across banks. This proposal has, however, been widely criticized by practitio...
2 Citations Source Cite
Published on Dec 1, 2016in Journal of Operational Risk 0.73
Jimi Hinchliffe1
Estimated H-index: 1
This paper explores the reasons for the pending demise of the advanced measurement approach (AMA) to operational risk, which was introduced through Basel II in 2004. The author identifies a number of drivers of the Basel Committee on Banking Supervision (BCBS) decision, and argues that, although the drivers of the BCBS decision to withdraw the AMA include failings on the part of banks, there have also been significant regulatory failures that have undermined the AMA. The author then considers th...
2 Citations Source Cite
Published on Oct 19, 2016in International Journal of Financial Studies
Suren Pakhchanyan1
Estimated H-index: 1
(University of Oldenburg)
Following the three-pillar structure of the Basel II/III framework, the article categorises and surveys 279 academic papers on operational risk in financial institutions, covering the period from 1998 to 2014. In doing so, different lines of both theoretical and empirical directions for research are identified. In addition, this study provides an overview of existing consortia databases and other publicly available sources on operational loss that may be incorporated into empirical research, as ...
3 Citations Source Cite
Published on Sep 1, 2016in Journal of Operational Risk 0.73
Giulio Mignola3
Estimated H-index: 3
(Intesa Sanpaolo),
Roberto Ugoccioni3
Estimated H-index: 3
(Intesa Sanpaolo),
Eric Cope1
Estimated H-index: 1
(Credit Suisse)
On March 4, 2016, the Basel Committee on Banking Supervision published a consultative document in which a new methodology, the standardized measurement approach (SMA), was introduced for computing operational risk regulatory capital for banks. In this paper, the behavior of the SMA is studied under a variety of hypothetical and realistic conditions, showing that the simplicity of the new approach is very costly in some ways. We find that the SMA does not respond appropriately to changes in the r...
2 Citations Source Cite
Published on Dec 1, 2015in China Economic Review 2.11
Jinmian Han1
Estimated H-index: 1
(Northwest University (China)),
Wei Wang1
Estimated H-index: 1
(Northwest University (China)),
Jiaqi Wang1
Estimated H-index: 1
(Northwest University (China))
This paper takes 533 operational risk loss events publicly announced by Chinese commercial banks in the period of 1995-2012 as the sample, using Peaks over Threshold (POT) model to quantify the operational risk. The statistical data classification indicates the internal fraud is the main type of operational risk in Chinese commercial banks. This paper explains its causes from the perspective of behavioral finance. The results are as follows: first, Chinese commercial banks' operational risk loss...
2 Citations Source Cite
Jianping LiXiaolei19
Estimated H-index: 19
(CAS: Chinese Academy of Sciences),
Xiaoqian Zhu7
Estimated H-index: 7
(CAS: Chinese Academy of Sciences)
+ 3 AuthorsYong Shi39
Estimated H-index: 39
(CAS: Chinese Academy of Sciences)
Risk aggregation considering inter-risk dependence has always been a challenge to both researchers and practitioners. The objective of this study is to formulate ways of aggregation of bank risks and comprehensively compare simple summation, variance–covariance and copula approach. Firstly, the three popular approaches are adopted to aggregate credit risk, market risk and operational risk of banks based on Austrian banking data. Then, two comparisons are mainly made. Total risks aggregated by di...
18 Citations Source Cite
Published on Sep 1, 2014in Journal of Operational Risk 0.73
Jianping LiXiaolei19
Estimated H-index: 19
,
Xiaoqian Zhu7
Estimated H-index: 7
+ 4 AuthorsWujiang Shi1
Estimated H-index: 1
Most advanced measurement approaches cannot simultaneously capture the overall dependence between operational risk components and be easy to use and understand. This paper proposes a mutual-information-based variance–covariance approach that is able to capture the overall correlation and is also highly tractable. Specifically, we replace the linear correlation coefficient with the global correlation coefficient in the framework of the variance–covariance approach. Originating from the theory of ...
8 Citations Source Cite
Published on Sep 1, 2014in Journal of Operational Risk 0.73
Stefan Kerbl1
Estimated H-index: 1
We explore a new data source of operational loss events, the Austrian Loss Data Collection, featuring more than 42 000 observations. We provide statistical characteristics per event type and business line and analyze the cross-time and crosssection of the data. Subsequently, we make use of the data to address a central question of operational risk research: which approach is best suited to model the severity distribution of a single loss? Scientific attention to this question resulted in three m...
4 Citations Source Cite
Published on Jun 1, 2013in Journal of Operational Risk 0.73
Jing Lu3
Estimated H-index: 3
,
Lei Guo1
Estimated H-index: 1
,
Xing Liu1
Estimated H-index: 1
2 Citations Source Cite
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